pep-04810 v1 CC-BY-SA-4.0
Blood-pressure-lowering peptide (FRVPTPN)
An experimental small peptide that blocks ACE, an enzyme that drives blood pressure up, so blocking it may help bring blood pressure down. Not an approved drug.
status
Someone proposed this peptide.
A researcher, an agent, or an algorithm wrote down the sequence and picked a target to hit.
A computer predicted how the peptide binds to its target.
An AI model like OpenFold3 or AlphaFold built a 3D structure and scored how well it fits the binding site.
Someone else ran the same prediction and got the same result.
A second contributor repeated the computation on their own hardware and the scores matched.
SYNTHESIZED — literature evidence
Literature-extracted sequence peptide — synthesized for bioassay as documented in linked reference(s)
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BIOASSAYED — literature evidence
Activity measured in linked reference(s) — IC50/MIC/cytotoxicity data
Fork this card to add platform evidence →
prediction metrics
ipTM0.698
pTM0.469
avg pLDDT79.5
ranking score0.776
STRUCTURE · PEP-04810 × ACE
ranking0.776
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
157
FRVPTPN
details
▸full evidence table2 metrics
| metric | value | tool |
|---|---|---|
| ipTM | 0.6984732151031494 | boltz-2 |
| ranking score | 0.7757956385612488 | boltz-2 |
▸structural qualityopenfold3
| metric | value | note |
|---|---|---|
| gpde | 2.083 | global PDE — lower = better |
| disorder | NaN | fraction disordered |
▸3-letter notation
Phe-Arg-Val-Pro-Thr-Pro-Asn
▸recipeboltz-2 1.0
| parameter | value |
|---|---|
| model | boltz-2 1.0 |
| weights | — |
| hardware | nvidia_nim_api |
| mlx version | — |
| python | — |
| random seed | — |
| msa strategy | none |
| diffusion samples | 1 |
| runtime | — |
| predicted by | mlx@peptide |
| predicted at | 2026-04-24 |
▸citationbibtex
peptidemodel (2026). Blood-pressure-lowering peptide (FRVPTPN) (pep-04810, v1). PeptideModel. https://peptidemodel.com/card/pep-04810
@peptide{pep04810,
sequence = {FRVPTPN},
target = {ace},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
} related peptides
references
discussion
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